Intelligent Transport Systems – From Research and Development to the Market Uptake. First International Conference, INTSYS 2017, Hyvinkää, Finland, November 29-30, 2017, Proceedings

Research Article

ITS Safety Ensuring Through Situational Management Methods

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  • @INPROCEEDINGS{10.1007/978-3-319-93710-6_15,
        author={Irina Makarova and Ksenia Shubenkova and Eduard Mukhametdinov and Vadim Mavrin and Dago Antov and Anton Pashkevich},
        title={ITS Safety Ensuring Through Situational Management Methods},
        proceedings={Intelligent Transport Systems -- From Research and Development to the Market Uptake. First International Conference, INTSYS 2017, Hyvink\aa{}\aa{},  Finland,  November 29-30, 2017, Proceedings},
        proceedings_a={INTSYS},
        year={2018},
        month={7},
        keywords={Autonomous vehicle Intelligent transport system Road transport Road safety Situational management},
        doi={10.1007/978-3-319-93710-6_15}
    }
    
  • Irina Makarova
    Ksenia Shubenkova
    Eduard Mukhametdinov
    Vadim Mavrin
    Dago Antov
    Anton Pashkevich
    Year: 2018
    ITS Safety Ensuring Through Situational Management Methods
    INTSYS
    Springer
    DOI: 10.1007/978-3-319-93710-6_15
Irina Makarova1,*, Ksenia Shubenkova1,*, Eduard Mukhametdinov1, Vadim Mavrin1, Dago Antov2, Anton Pashkevich2
  • 1: Kazan Federal University
  • 2: Tallinn University of Technology
*Contact email: kamIVM@mail.ru, ksenia.shubenkova@gmail.com

Abstract

The paper is devoted to solving the problem of complex organizational and technical systems’ (COTS) safety improvement with the use of methods of situational management. The possibility to manage COTS with the use of decision support system, the intelligent core of which contains the object model of the precedent describing unwanted processes in the COTS, is considered. The object-oriented hierarchy model of the vehicle as a complex dynamical system is developed. To improve reliability and safety of the COTS the database of precedents is established. Precedents in this database are presented according to the developed model by which the analogues search procedure is performed and the best managerial decision is chosen.